---
abstractspacing: double
fontsize: 12pt
margin: 2cm
urlcolor: darkblue
linkcolor: Mahogany
citecolor: Mahogany
spacing: single
bibliography: references.bib
biblio-style: apalike
output:
  pdf_document:
    citation_package: natbib
    fig_caption: no
    number_sections: no
    keep_tex: no
    toc: no
    toc_depth: 3
    template: article-template.latex
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE,cache=TRUE)
knitr::opts_chunk$set(fig.width=7, fig.height=6, out.width = '70%', fig.align = "center") 
rm(list=ls())
library(remotes)
library(kableExtra)
library(haven)
library(tidyverse)
library(ivmodel)
library(doParallel)
library(foreach)
library(estimatr)
require(AER)
library(lfe)
library(glue)
#path <- "/Users/ziwenzu/Dropbox/research/IV/IV Sensitivity/LLXZ_rep"
path <- "~/Dropbox/ProjectZ/IV Sensitivity/LLXZ_rep"
setwd(path)
knitr::opts_knit$set(root.dir = path)
#install_github("apoorvalal/ivDiag")
library(ivDiag)
# number of cores
cores <- 15
```

## @goldstein2017

| Replication Summary | |
|---------|---------------------|
| Unit of analysis | city |
| Treatment | lobbying spending |
| Instrument | direct flight to Washington, DC |
| Outcome | total earmarks or grants awarded |
| Model | Table4(4)|

```{r ajps_Goldstein_etal_2017}
df <- readRDS("./rawdata/ajps_Goldstein_etal_2017.rds")
df <- as.data.frame(df)
Y <-"ln_recovery"
D <-"ln_citylob"
Z <- c("direct_flight_dc", "diverge2_r")
controls <- c("pop_r", "land_r", "water_r", "senior_r", "student_r", "ethnic_r",
              "mincome_r", "unemp_r", "poverty_r", "gini_r", "city_propertytaxshare_r", 
              "city_intgovrevenueshare_r", "city_airexp_r", "houdem_r", "ln_countylob")
cl <- "state2"
FE <- "state2"
weights <- NULL
(g<-ivDiag(data=df, Y=Y, D=D, Z=Z, controls=controls, FE =FE, 
  cl =cl, weights=weights, cores = cores, parallel = TRUE))
```

```{r, cache = FALSE}
plot_coef(g)
```


```{r ajps_Goldstein_etal_2017_sav, echo = FALSE}
save(g, file="./estimate/Goldstein2017.RData")
```
